High resolution autonomous precision positioning system

ABSTRACT

A vehicle navigation system employing echo or doppler analysis to provide autonomous or enhanced navigational capabilities by correlating stored scene information with echo analysis information derived from an Active Traveling-Wave Device (ATWD) output representing information concerning the vehicle&#39;s state and velocity vectors with respect to a mapped scene. The system employs a steppable frequency oscillator for providing a signal which is stepped in frequency to provide an increased range resolution of the ATWD.

[0001] This is a Continuation-In-Part of application Ser. No. 09/779,723filed Mar. 7, 2001, which is a Divisional of application Ser. No.09/419,767 filed Oct. 18, 1999, which is a Divisional of applicationSer. No. 08/880,362, filed Jun. 23, 1997 (now U.S. Pat. No. 6,018,698)which is a Continuation of application Ser. No. 08/251,451, filed May31, 1994 (now U.S. Pat. No. 5,654,890), the disclosures of which areincorporated herein by reference.

FIELD OF THE INVENTION

[0002] The present invention is directed to an autonomous precisionapproach and landing system (APALS) for enabling low visibility flyingand landings at airports, as well as a method and system for achievingprecision positioning and navigation for a variety of vehicle typesusing doppler or echo analysis in sonar, radar, laser, and other suchdevices.

BACKGROUND OF THE INVENTION

[0003] Current industry practice for low-visibility landings isdependent on airport ground equipment and inertial navigation equipment.These techniques are limited to landings at those runways which areequipped with highly reliable transmitters of radio frequency localizerand glide slope information. These existing systems either land theaircraft using an automatic pilot or aid the pilot in landing theaircraft by providing the pilot with autopilot control commandsdisplayed on a Head Up Display (HUD).

[0004] It has been suggested that future systems make use of informationreceived from the Global Positioning System (GPS) in conjunction withon-board Inertial Navigation systems (INS) to generate the necessaryprecise navigation for landing. However, in addition to the externalsatellites required for GPS, these systems are currently envisioned torequire ground stations at known locations near the runway for thedifferential precision necessary for landing. Other proposed systemsprovide the pilot with a real time image of the runway scene as derivedfrom millimeter wave (MMW), X-Band, or infrared (IR) frequencies.

[0005] The following are further examples of navigation systems known inthe art.

[0006] U.S. Pat. No. 5,136,297 to Lux et al discloses an autonomouslanding system. The Lux patent includes a navigation unit employed inthe system which includes a sensor, flight position data, an imagecorrection unit, a segmentation unit, a feature extraction unit and acomparison unit. The Lux patent discloses that a comparison is conductedas to whether or not a sequence of features in the overflight path imagepattern agrees with features found in a reference store, such as mapdata which is stored in the system. Further, Lux discloses the use of aradar navigation system for use as a sensor in the system.

[0007] U.S. Pat. No. 4,698,635 to Hilton et al discloses a radarguidance system coupled to an inertial navigation apparatus. The systemincludes a master processor, a radar altimeter, a video processor, amemory and a clock. The memory has stored therein cartographic map data.

[0008] U.S. Pat. No. 4,495,580 to Keearns is cited to show a navigationsystem including a radar terrain sensor and a reference map storagedevice for storing data representing a terrain elevation map.

[0009] U.S. Pat. No. 4,910,674 to Lerche discloses a navigation methodwhich includes a correlator for comparing terrain reference data withprocessed altitude data obtained with a wave sensor.

[0010] U.S. Pat. No. 4,914,734 to Love et al is cited to show amap-matching aircraft navigation system which provides navigationalupdates to an aircraft by correlating sensed map data with storedreference map data.

[0011] U.S. Pat. No. 4,891,762 to Chotiros is cited to show a patternrecognition system for use in an autonomous navigation system.

[0012] The above-mentioned prior systems suffer from one or more of thefollowing problems:

[0013] 1) Reliance on ground-based systems for precise terminal landinginformation severely reduces the number of runways available for Cat IIIa and b landings (currently 38 runways in the U.S.).

[0014] 2) Reliance on GPS and differential ground transmitters for GPScreates a need for currently rare ground equipment and a lack ofreliability (based on the military nature of GPS). The GPS is a militaryprogram owned, operated, and paid for by the United States Air Forceoriginally intended for military navigational purposes and is designedso that civilian use can be made of it but at a reduced accuracy. Themilitary uses a very special code which gives them better accuracy, thatis called the P code. The normal civilian code is called the C codewhich is good to about 30 m in accuracy; however, the military retainsthe right to disable the C code to the point where the accuracy goesdown to about no better 100 m. This is what the military refers to as“selective availability” so that in time of conflict they can turn onselective availability and deny the enemy the ability to navigate betterthan 100 m. There are a number of schemes for getting around theinaccuracies imposed by the military. However, the Air Force hasmaintained a position that they are against any of these schemes whichimprove the accuracy when they are trying to make it inaccurate.

[0015] The lack of reliability is also a result of the fact that, inorder to be accurate, at least four satellites must be present in theoverhead view; and, if one of the four satellites fails, then theaccuracy will be degraded. Thus, the reliability is not just based onthe on-board equipment, i.e., the GPS receiver, but it is also based onthe reliability of the satellites themselves.

[0016] 3) Additional sensors, such as MMW and IR, currently envisionedfor systems to provide pilots with the “situational awareness” necessaryto successfully land in low visibility conditions are expensiveadditions to the on-board flight equipment and are marginal inperformance. MMW real-beam radars provide “grainy” low resolution imageswhich are difficult to interpret and IR systems cannot penetrate in manytypes of fog that cause the “low visibility” in the first place.

SUMMARY OF THE INVENTION

[0017] It is therefore an object of the present invention to overcomethe problems associated with the prior approach and landing systems.

[0018] It is another object of the invention to provide an approach andlanding system which provides low visibility take-off and landingassistance for several classes of aircraft.

[0019] It is another object of the invention to provide safe landing ofgeneral aviation and transport aircraft (covered by parts 25, 91, 121and 125 in the Code of Federal Regulation) in low visibility conditions[Category II, IIIa, and IIIb defined by the Federal AviationAdministration (FAA)] without dependence on high reliability groundtransmitting equipment.

[0020] These and other objects are accomplished by the present inventionwhich provides an Autonomous Precision Approach & Landing System thatmakes use of radar echoes from ground terrain and cultural (man made)targets to provide the on-board Inertial Navigation System with accurateaircraft position and velocity updates. According to the invention,these measurements come from a modified standard X-band, low-resolutionweather radar.

[0021] Other objects of the invention include providing high precisionpositioning and navigation capabilities for vehicles with radar, sonar,laser, or other such devices that use doppler or echo analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022]FIG. 1 is a block diagram of the APALS system according to theinvention.

[0023]FIG. 2 is a waveform diagram according to the invention.

[0024]FIG. 3 is a circuit diagram of a modified weather radar deviceaccording to the invention.

[0025] FIGS. 4(a)-4(e) illustrate steps of APALS Synthetic ApertureRadar (SAR) processing according to the invention.

[0026]FIG. 5 shows a reference scene and a corresponding Radar Mapaccording to the present invention.

[0027]FIG. 6 illustrates a Generalized Hough Transform Map-MatchAlgorithm employed in the present invention.

[0028]FIG. 7 illustrates a Navigation Solution according to an exampleof the invention.

[0029]FIG. 8 illustrates a Head-up Display (HUD) according to thepresent invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

[0030] Several of the important features of the APALS system accordingto the invention are set forth below:

[0031] A. Modified Weather Radar: The modification to a conventionalweather radar allows the modified weather radar to make high resolutionsynthetic aperture maps of overflown terrain.

[0032] B. Area Correlation: This refers to the application of matchingsynthetic aperture radar maps with previously stored references tolocate specific spots on the ground near a path to a specific runway.

[0033] C. Range/Range Rate Measurements Integrated Into Kalman Filter:This refers to the application of using high resolution radar range andvelocity measurements of specific, but not augmented, spots on theground of known location to update a navigation system using Kalmanfiltering.

[0034] D. Situational Awareness Display Format: This refers to theapplication of precise navigational information to provide the pilotwith a “situational awareness” display of sufficient accuracy to allowthe pilot to land the aircraft in low visibility conditions in the samemanner as if (s)he were using his/her judgement to land the plane ingood visibility conditions.

[0035] Each of the above features is discussed in detail below.

[0036]FIG. 1 is a block diagram of the APALS system according to thepresent invention. The other NAV Aids 2 refers to the navigation aidsthat are conventionally employed on any aircraft and include, forexample, a VOR (VHF omnidirectional radar), DME (distance measuringequipment) receiving equipment, the artificial horizon, the verticalgyro, the airspeed indicator, and the altimeter (which is a barometricaltimeter). The APALS processor 16 will make use of this information inorder to monitor the reasonableness of the APALS estimate concerning thestate of the vehicle, which is the outputX from the processor 16. Theabove-discussed elements all interface to the system over a standardinterface bus such as the known ARINC 429 bus.

[0037] The INS (inertial navigation system) or IMU (inertial measurementunit) 4 are inertial instruments that measure the translationalaccelerations and the angular rates. There are several different IMU'sthat can be employed in APALS, one of which is, for example, a Bendixunit known as the Bendix mini-tact IMU.

[0038] The GPS receiver 6 is a special receiver that is designated toreceive the satellite signals and deduce from those satellite signalsthe position and velocity of the aircraft. There are several models thatcan be used for this, but there is only one or two at present that havepassed the FAA requirements for primary navigation equipment on-board anaircraft.

[0039] The weather radar 8 which is also equipment that will already beon-board the aircraft is, according to the invention, as will bediscussed below modified. For example, the Honeywell Primus 870 made byHoneywell may be employed. This radar is a non-coherent radar so itwould have to be modified with a new receiver and transmitter to make itcoherent. The weather radar 8 provides a range R, and range rate R′outputs to the Processor. The weather radar 8 receives a radar frequencycontrol signal from the Processor 16 which will be discussed below inconnection with the radar modification shown in FIG. 3.

[0040] The scene data base 10 is a data base created by going todifferent airports that will use the system and making flights duringwhich the radar signatures of the ground returns are measured. Further,aerial photographs are taken to use together with the radar data to makereferences which would then be used to compare against the radar returnsthat will occur when the actual low visibility landing is taking place.

[0041] The display generator 12 and the display 14 are typicallysupplied by the manufacturer of the device known as a Head-Up Display(HUD), which is what the APALS uses as a see through device that allowsthe pilot to view the outside world, and see the APALS display in frontof him or her. The pilot will see a virtual runway even when the actualrunway is obscured by, for example, clouds or fog. Suitable HUD's arecurrently built by GEC Avionics (Great Britain), Flight Dynamics, Inc.(Portland, Oreg.) and Sextant Avionique (France). The actual APALSoutput is a vector labeledX and consists of the position, velocity andattitude information of the aircraft as best determined by the APALSsystem. The display generator typically takes that information andgenerates what the outside world scene would look like from thecurrently estimated state of the aircraft,X.

[0042] The processor 16 receives inputs from elements 2, 4, 6, 8 and 10and outputs vectorX There are a number of known processors that can beused for APALS.

[0043] A. Modified Weather Radar:

[0044] The radar modification consists of applying randomized steppedfrequency pulse compression to allow a range resolution of 4 meters(even though a pulse length of 2 μsec would normally limit rangeresolution to 300 meters). The waveform consists of a series of pulsesat the normal higher Pulse Repetition Frequency (PRF) of the weatherradar (˜3000 Hz). The first 160 pulses are randomly stepped in frequencyso that each pulse is at a different frequency. Any one pulse, however,stays at a constant frequency for its entire 2 μs duration. This isimportant because it allows the precision measurements to be madewithout modifying the band-pass characteristics of the radar receiver.The frequencies are such that there are 160 different frequenciesspanning 40 Mhz in 250 Khz steps. Over the time of each set of 160pulses, the 40 Mhz spectrum is completely filled. The order of the stepsis randomized to avoid ambiguities. A diagram of the waveform is shownin FIG. 2. The step size is 250 Khz which corresponds to a 4 μs or 600meter “coarse” range bin. This wider (than 2 μs) coarse bin was chosento eliminate any ambiguities from adjacent pulse “spillover” energy. Thewaveform can be as long as necessary to integrate returns for a preciseDoppler measurement.

[0045] The waveform is extended to multiples of 160 pulses because 160is the number of pulses required to cover the 40 MHz bandwidth neededfor 4 meters range resolution. In this case the integration time islimited to 0.25 seconds since, at X-band, it will yield a velocityresolution of 0.07 m/sec. which is a sufficient accuracy to update thenavigation Kalman filter.

[0046]FIG. 3 shows a typical implementation of generating the waveformby adding a steppable frequency oscillator 17 to the weather radar. Asshown in FIG. 3, the majority of the circuits of radar (transmitter 18,frequency multipliers, dividers and mixers 20, receiver 22, duplexer 24)remain unchanged. The integration of the modified waveform into theweather radar from each of the different radar manufacturers will beunique.

[0047] Processing the waveform to achieve the desired resolution (4 m inrange and 0.07 m/sec. in Doppler) is accomplished in a highly efficientmanner because the image is being taken of just one short segment ofrange (where the beam intersects the ground). The “picture” or map willextend 160 meters or 40 pixels in range and therefore is contained inone 600 meter “coarse range”. This is in effect “zoom processing” of theregion which is very efficient. The application of zoom processing tothis unique waveform allows very high resolution to be achieved withvery minor physical modifications to a normally low resolution radar.

[0048] Motion Compensation: The Synthetic Aperture Radar (SAR) map thatis required for this system to work well covers a small area and theaccuracy of the vehicle motion required is within the bounds of theknowledge of the system. This is because the navigation portion of thesystem will have very precise knowledge of the state of the vehicle'smotion relative to the earth as will be discussed below.

[0049] The following delineates the steps required for the twodimensional zoom processing of APALS.

[0050] As described in the waveform of FIG. 2, during the integration of0.25 seconds there are 4000 pulses. This large integration time isbroken down into 25 sub-intervals or “words” of 160 pulses each (FIG.2). During each sub-interval, the full bandwidth of 40 MHz istransmitted by having each pulse at a different frequency taken, atrandom, from a set of 160 frequencies spaced 250 KHz apart. If thelowest frequency were 9 GHz, the sequence of frequencies would be: 9.000GHZ, 9.00025 GHz, 9.005 GHz, 9.00075 GHz, 9001 GHz . . . 9.040 GHz. Theorder is scrambled randomly for reasons which will be explained below.

[0051]FIG. 4(a) is a wave diagram for explaining a coarse range bin.With a 2 μs pulse (1), if the receive signal (2) and (3) is sampled thesame time delay after the transmit pulse, those returns will allrepresent targets or ground clutter from the same range. Since the pulseis 2 μs wide, the energy at the time of the sample will come from 150meters in front of to 150 meters behind the point on the ground with atime delay of the sample center. The processing chosen covers a 600meter region centered at the time of the central return. While thereshould be no return in any area beyond ±150 meters, there may bespill-over from other bright reflectors and by processing the widercoarse bin, the possibility of ambiguous foldover is eliminated.

[0052] To simplify the explanation, a “linear” rather than a randomfrequency sequence is examined. In FIG. 4(b) it is seen that thesamples, each being from a different pulse in the chain of 4000 pulses,range in frequency from f1 to f160 and then f1 to f160 is repeated forthe next 160 Pulse Repetition Intervals (PRI's) and so on for 25sub-intervals until 4000 pulses have been transmitted and 4000 receivesamples have been gathered. As shown from FIG. 4(b), processing the 4000samples into a range profile of fine 4 meter bins is nothing more thansumming the sample values that come from the same frequency (there are25 of them). and using the sum as one of the inputs to an InverseDigital Fourier Transform (IDFT), and representing that process for all160 frequencies. A Fourier transform is a process of taking samples intime of a waveform and determining how much energy there is at eachfrequency and the inverse of the process is taking samples of energycontent at different frequencies and producing what the waveform lookslike as a function of time (time is equivalent to range for a radarecho).

[0053] The example given above and in FIG. 4(b) is a simplification thatwould work well if there were no motion between the radar and ground. Inorder to describe what is necessary for APALS to accommodate motion, itis necessary to introduce the concepts of phase and phase compensation.

[0054] The phase of a radar signal depends on two items, the frequencyor wavelength of the signal and the distance from the transmitter. Thisis shown in FIG. 4(c). Radar waves are variations in local electric andmagnetic fields which can be represented by the sine wave shown in FIG.4(c).

[0055] The distance from one peak to another is called the wavelengthand is determined by the frequency of the transmitted signal. FIG. 4(c)shows a Receiving Object whose distance is 5-¼ wavelengths away from theTransmitter. The whole number of wavelengths is not important to phasebut the remainder or fractional part is the phase difference betweenwhat is sent and what is received. In FIG. 4(c), the phase difference is¼ of one wavelength or 90° (one wavelength is characterized by one fullcycle of 360°). If the receiving object simply reflected the signal backto a Receiver co-located with the transmitter, as is the case withradar, the distance and, therefore, the phase shift is doubled to 180°.

[0056] The phase of the returns from different samples but off of thesame stationary object will change with a frequency hopped radar such asAPALS. FIG. 4(d) shows the effect of changing wavelength on phase. InFIG. 4(d), even though the transmitter and the receiving object are thesame distance apart as they are in FIG. 4(c), the phase has increased to180°, one way. In FIG. 4(d) there are 5-½ wavelengths in the singlepath-length.

[0057] As the frequency of the pulses increases (FIG. 4(b)), thewavelength gets shorter and the phase difference increases. It isprecisely this change in phase as a function of frequency that allowsthe IDFT to discern the ranges of object from the frequency content ofthe return samples. The samples, by their nature, contain both a measureof the energy and a measure of the phase difference of the return from apulse of a particular frequency.

[0058] Relative motion between the Transmitter and the Reflecting Objectcauses a phase shift with time which causes a phase shift from pulse topulse as shown in FIG. 4(e).

[0059] This phase shift as a function of time is known as the Dopplereffect. The measurement of this rate of change of phase or Doppler iswhat allows APALS to update range rate as well as range for the inertialsystem after each map-match. It is also what creates the need for phasecompensation.

[0060] It is important to note that the phase changes due to increasingfrequency have the same characteristic as the phase changes due toincreasing distance between the transmitter and the reflecting object.In both cases, the phase changes will increase steadily with time. Thisis the ambiguity that was mentioned earlier. As long as the frequenciesare stepped in order from pulse to pulse, the IDFT will not be able todistinguish between distance of the Reflecting Object and the speed ofthe Reflecting Object. This is because the distance information iscontained in the phase differences of the reflections off a singleobject at different transmit frequencies.

[0061] To obviate this ambiguity problem, the frequencies are notstepped in order of increasing frequency as shown in FIG. 4(b), butrather randomly. This breaks the linearity of the phase changes withtime due to frequency shifting so that it can be separated from thealways linear changing phase that is due to constant velocity motion. Itis still necessary to present the sampled values of the return signal tothe IDFT in order of increasing frequency so the order of frequenciestransmitted must be kept track of. This is accomplished in APALS byusing a pre-stored pseudo-random frequency order which is 4000 elementslong.

[0062] Once the relationships between distance, phase, and velocity areunderstood in the context of the APALS waveform as described above, thephase compensation and processing for APALS can be concisely explainedin the following steps:

[0063] 1) The received waveform is converted to a set of digital sampleswhich preserves both signal strength and phase difference. This processis well known in the art as in-phase and quadrature sampling or I & Qsampling. The digital samples are stored temporarily and tagged bothwith their order in time of reception and with their frequency order.

[0064] 2) The coarse range of interest is identified by the system basedon the desired map area, and the samples which come from thecorresponding delay are singled out for processing.

[0065] 3) The Doppler frequencies are determined for the desired maparea, and the center frequencies for the Doppler bins to be processedare determined.

[0066] 4) For each Doppler bin, the set of samples is arranged in orderof the transmit frequency which generated it, and presented for phasecompensation prior to being sent to the IDFT.

[0067] 5) For each Doppler bin the phase rotation for each transmitfrequency and each receive time is calculated and that phase issubtracted from each sample according to its time order and adjusted forits wavelength based on its transmitted frequency. The net effect isthat motion is taken out of the samples that are moving at the precisevelocity that is the designated center of the Doppler bin or filter.Objects that are moving faster or slower will not “add up” because thephases of their samples will not be recognized by the IDFT.

[0068] In order to prevent smearing, due to accelerations which changethe velocity during the 0.25 second dwell, the compensating phaserotations must be calculated based, not on a constant velocity, but on avelocity modified by the aircraft's accelerations. These accelerationvalues are readily available in the APALS system because they are partof the accurate state vector which is calculated by the navigationfilter.

[0069] B. Area Correlation

[0070] The APALS system uses the Scene Data Base 10 for pre-storedscenes as references with which to compare the radar maps that areproduced through the weather radar. The radar maps can be thought of ascomprising resolution “cells” whose dimensions are range resolution inthe down range direction and range rate resolution in the cross rangedimension. Down range direction is simply the radial distance from theaircraft. In a radar system the normal way of mapping with a radar is tocut the return up into pieces that are returns coming from differentranges. This is because the radar is capable of measuring range by thetime delay of the return. The down range dimension is always thedistance radially away from the radar. The present system is typicallylooking at 45° right or left and so the down range dimension is a linegoing 45° off the nose of the aircraft. The cross range dimension is thedimension that is directly orthogonal or at 90° to the down rangedimension. It is not always exactly 90°, in the present system it ismeasured by changes in the doppler frequency of the return. Thefrequency of the return is dependent on the relative velocity in thedirection of that return. The contextual information in the radar map iscompared to that of the reference. When a match is found for each pointof ground represented by a cell in the reference, the range and rangerate of the sensed scene are known with respect to the aircraft. Sincethe location of at least one point in the scene is known precisely withrespect to the desired touch down point, by simple vector subtraction,the range rate to the touch down point is calculated. There are twoaspects to generating this important information:

[0071] 1) Generating a reference which will allow a locally unique matchto the radar map.

[0072] 2) Using a correlation algorithm that efficiently “fine-tunes”the match point to a 1-cell accuracy and provides a “measure ofgoodness” or confidence in the match.

[0073] The references for APALS are generated from aerial photographsthat have been digitized or scanned into a computer and from SAR maps.The SAR maps are taken in two swaths, one on either side of the finalapproach trajectory, that are centered 1 mile offset of the aircraft'strajectory (ground projection). Software is used to match points in theaerial photo with coordinates of a pre-stored navigation grid so thatthe location of any point in the photo is known relative to the runwaytouch down point (no matter how far the scene is away from the runway).The key features of these references are that they are simple and thatthey rely on prominent cultural and natural features which produceconsistent radar returns that are distinguishable as lines with a uniqueshape. The two types of features to have these characteristicsconsistently are the corners made by a building face and the ground, androads.

[0074]FIG. 5 shows a typical reference and the corresponding radar map.In this case the dots represents a specific pattern of a highwaycrossing. Such simple references are found to work well when used withthe map matching algorithm well known in the art as the “generalizedHough transform” which is described below.

[0075] The correlation algorithm used for map matching in the APALSsystem is the well known generalized Hough transform. The Houghtransform is incorporated in several image processing techniques in usetoday, especially in military applications. In general, the Houghtransform is a computer method typically used to find a line or othersimple shapes/patterns in a complex picture. This scene matchingalgorithm is advantageous in that:

[0076] a) It requires very few points to be compared, (i.e., much lessthan the total in the scene).

[0077] b) It requires the computer to perform only the mathematicaloperation of adding and avoids the other more time consumingmathematical operations.

[0078] In FIG. 6(A) a simple reference is shown to the left and a verysparse sensed scene (just two points) is shown to the right. Thealgorithm works such that every point in the sensed scene is operated onin the following manner:

[0079] 1) Each point in the reference is tried as the particular sensedpoint.

[0080] 2) As each point in the reference is tried, the position that the“nominated point” (black point in the reference) occupies in the sensedscene is recorded. This is shown in the sequence of scenes in FIG. 6b.After all the points in the reference are used, the set of recorded“nominated” points in the sensed scene is an “upside-down and backwards”replica of the reference scene, rotated about the “nominated point”.This reversed image is shown above the last block of FIG. 6b.

[0081] 3) As all the points in the reference are operated on, the pointin the scene with the most accumulated nominations is designated as thematch point. This is illustrated in FIG. 6c.

[0082] C. Range/Range Measurements Integrated Into Kalman Filter

[0083] The measurements being made by the radar are the magnitude of therange vector and the magnitude of the range-rate vector from theaircraft to a specific point in the map match scene. If at least threeof these measurements were being made simultaneously, one could solvefor the three elements of aircraft velocity explicitly. This solution isshown in FIG. 7. The sequence of measurements being made in FIG. 7 arethe range and range-rate to known points on the ground. The way in whichthese are used is exactly analogous to the way the global positioningsystem works with satellite measurements. For example, consider threesatellites that are displaced in the sky angularly. If the range and therange rate to those satellites are known, then the components of boththe position vector and the velocity vector of the position relative tothose three satellites can be solved. Beyond that, it is necessary todepend on information stored on the satellites and transmitted down sothat it can be determined where they are. Then the position can bededuced. The difference in APALS is that the APALS system takes picturesof the actual ground and compare the taken pictures to stored maps. Onceit compares them to stored maps and finds the match point, the matchpoint is immediately known in terms of its range and range rate at thatpoint of time. As APALS progresses it measures its range and range raterelative to known points on the ground. Once it measures three differentpoints, it can form a deterministic solution of where it is, in whatdirection it is heading and how fast it is going. The sequence in APALSis a little different in that it does not obtain a good geometric casein close time proximity. Rather, it flies along mapping from side toside but not getting that third one. It repetitively gets the range andrange rates on each side, and over time forms a very accurate solutioniteratively to the equation that allows it to know its position vectorand velocity vector.

[0084] Since, however, the measurements being made by radar areseparated in time by as much as 4 seconds, it is necessary to solve forthe components of the vectors recursively, over time, through the use ofa Kalman statistical filter. The Kahnan filter uses data from aninertial navigation system INS, or an inertial measurement unit (IMU) 4(FIG. 1) to determine the motion of the aircraft between measurementtimes. The INS or IMU 4 is more than just the inertial instruments, butthe complete collection of inertial instruments and computer that resultin a navigation solution including position and velocity. An INS istypically only used on commercial aircraft today that traverse theocean.

[0085] D. Situational Awareness Display Format

[0086] The raw output of the APALS system is a very accurate estimate ofthe “state vector” of the aircraft in a coordinate system that has itsorigin at the desired touch down point on the particular runway that istargeted. This knowledge of position, velocity and attitude are providedas a “situational awareness” display which the pilot can effectively useto safely land the aircraft. This is accomplished primarily bydisplaying a conformal, properly positioned runway outline in properperspective to the pilot on a Head-up Display (HUD). In clear weatherthe image will overlay that of the actual runway edges as the pilotviews the runway through the wind screen. The appropriate touch-downzone will also be displayed (conformably), thereby providing the pilotsituational awareness such that (s)he may land his/her aircraft in thesame manner as (s)he would in visual meteorological conditions (VMC).The use of the HUD allows the pilot the earliest possible view of theactual visual scene on the way to touchdown. The precise navigationalknowledge of the APALS system together with the radar altimeter allowsfor the generation of a “flare cue” to tell the pilot when and how toflare for a precise, slow descent-rate touchdown.

[0087] The key aspect in being able to land using situational awarenessis the display of the conformal runway symbol and extended center-linein a context which also includes conformal symbols of the horizon line,flight path vector, and 3° glide slope indicator. The display of thesesymbols can be derived from APALS navigation knowledge or from otheraircraft instruments. FIG. 8 shows a particular symbol set with theaddition of the synthetic runway image.

[0088] True ground speed information in the APALS system is sufficientto generate moving segments in the extended center-line to create asensation of “speed” for the pilot.

[0089] A secondary aspect of APALS is that the X-band radar togetherwith APALS enhanced resolution can detect runway incursions prior tolanding in low visibility conditions. This is accomplished with a broadsweeping ground map just prior to landing which is similar to the“ground map mode” of a conventional weather radar. The notable exceptionis that the range resolution is two orders magnitude sharper than thatof the conventional weather radar. This allows large objects, such as ataxiing aircraft to be resolved into more than one pixel. As a result,the APALS is able to correctly distinguish and separate larger andsmaller objects from each other. When this is coupled with the precisenavigational knowledge of APALS, any radar returns can be related totheir precise location in the airport scene to determine if they are ahazard.

[0090] As set forth above, the APALS system does not depend on groundequipment installed at a particular airport. It therefore offers thepotential of low-visibility landings at many airports than are currentlyunavailable for such landings because they do not have the groundequipment of sufficient reliability to support the automatic landingsystems.

[0091] Further, APALS does not require the addition of any new visionsensors on the aircraft or installations on the ground and thereforeinstallation costs are minimum. The accuracy and reliability of thedisplay can be checked and verified during normal visual operations. Itcan also be routinely used for training at any airport during normalvisual operations. In addition, it can detect runway obstacles prior tolanding without adding any sensors.

[0092] The display for APALS can be either head-up or head-down.

[0093] The waveform can be varied in PRF (Pulse Repetition Frequency),pulse width, bandwidth, and integration time to affect changes inresolution and processing dynamic range. The Pulse Repetition Frequencyis the number of pulses per second that the radar transmits. This isimportant because the PRF determines the amount of average power thatthe radar receives. It also determines what kind of ambiguities thereare in range.

[0094] Those skilled in the art will understand that variations andmodifications can be made to system described above, and that suchvariations and modifications are within the scope of the invention. Forexample, different scene match correlation algorithms and differentnavigation filters (other than Kalman) such as neural net “intelligent”estimators can be used without changing the nature or concept of thepresent invention.

[0095] The fundamental navigation technique disclosed herein remainsunchanged if the map references are determined while performingin-flight navigation and such “scenes of opportunity” used in mapmatching a short time later to compare the inertial navigation with thechange in range and range rate of a reference point within such scenesof opportunity. Such data will allow the navigation filter to update thevelocity vector and to update the position vector with the relativeposition change between repeated measurements of such scenes ofopportunity instead of absolute position data. These measurements mayconducted using any convenient reference frame, not just thoseassociated with the preferred APALS stored maps. Such variations of theinvention would be useful in bounding the drift associated with rawinertial measurement unit components such as gyroscopes andaccelerometers when pre-surveyed scene data is not available.

[0096] A variation apparent to those skilled in the art is the use ofmultiple correlations of a scene over the time it is within view. Afurther variation would be to select unsurveyed scenes-of-opportunity inreal-time for these repeated measurements, allowing the navigationfilter to utilize the changes in range and range rate data to fullyupdate the velocity vector but only update the position vector on arelative motion basis. Such implementations of the invention would beuseful in stabilizing the drift associated with inertial measurementunit components such as gyroscopes and accelerometers. Such repeatedmeasurements can be conducted using any reference frame, not just thoseassociated with the preferred APALS stored map.

[0097] As mentioned above, the concepts of the present invention willalso apply to any Active Traveling-Wave Device (ATWD) that is structuredand operated in a manner analogous to that discussed above with regardto the APALS radar system. As used herein, an ATWD is a device thatemploys doppler or echo analysis, and whose emissions can becharacterized by the three dimensional electromagnetic wave equationsor, when applied to non-electromagnetic phenomena, their simplificationwhen electromagnetic concepts for Potential, Current, Resistance,Inductance, Conductance, and Capacitance are directly replaced (oromitted) by analogous (or non-existent) quantities. In other words, inaddition to radar systems, the term ATWD encompasses devices that emitand receive acoustics (i.e., SONAR) through gas and liquids and solids;devices that emit and receive heat (e.g., infrared); and devices basedon light (LASERs).

[0098] For example, for a SONAR system which transmits sound waves inwater, current would be analogous to pressure, potential would beanalogous to velocity, capacitance would be analogous to mass per unitvolume, inductance would be analogous to compressibility, andconductance and resistance have no equivalent. This includes thewaveforms, and calculations that are derived from the echo returns, etc.with the exception that the speed of light in all the equations must bereplaced by the speed of sound in the liquid, such as sea or freshwater. For SONAR, refraction can be more of a problem, but at highersonar frequencies and short range-to-target distances, even refractiondistortions in water can be ignored.

What is claimed is:
 1. A method of vehicle navigation using an ActiveTraveling-Wave Device (ATWD), comprising: storing information ofcultural or natural features in an area of the ground along and to thesides of a movement path; using said ATWD during movement to obtaininformation of said features along and to the sides of said movementpath; comparing said stored information with said information obtainedby ATWD to determine successive different match points representingdifferent ground locations; determining the range and range rate of eachof said match points; determining the vehicle's location and velocitybased on repetitive range and range rate measurements of said matchpoints; and supplementing the accuracy of said vehicle location andvelocity determination with information from at least one of anadditional navigation unit.
 2. The method according to claim 1, whereinsaid additional navigation unit information is based upon integration ofinertial measurements.
 3. The method according to claim 1, wherein saidadditional navigation unit information represents locations between saidmatch points and is combined with said vehicle location and velocitydetermination using Kalman statistical filtering.
 4. The methodaccording to claim 1, wherein a phase changing effect created by motionof said navigation unit is reduced, said method comprising: definingsubsets of a frequency hopped signal and a center frequency for eachsubset, bouncing said signal off of an object and receiving a reflectedversion of said signal to determine a phase change in said signal;receiving reflected versions of said subsets, and subtracting phaseeffects from the samples that correspond to the center frequency of saidsubset.
 5. The method of claim 1 wherein phase compensation is providedfor said ATWD, said method comprising: storing a pseudo-random frequencyorder; changing a transmission frequency of an ATWD signal based on saidpseudo-random frequency order; using said pseudo-random frequency orderto sample a received ATWD signal which is a reflected version of saidtransmitted ATWD signal, thereby generating samples of said receivedATWD signal; and reordering said samples in an order of increasingfrequency.
 6. The method according to claim 5, further comprising:summing a group of said samples; and performing an Inverse DigitalFourier Transform on said samples to develop a range value.
 7. Themethod according to claim 5, further comprising: tagging said receivedsamples with information of their order of reception and order offrequency; identifying a coarse range of interest based on a particulararea of the ground; selecting the received samples which correspond tosaid particular area; defining signal subsets of said transmittedsignal; determining the Doppler frequencies for said selected samplesand center frequencies for the subsets corresponding to said particulararea; arranging said received samples in frequency order within saidsubsets; calculating the phase rotation due to transmit frequencies andreceive times for the received samples, and subtracting said phaserotations from the corresponding samples according to the time order andwavelength of said sample, to thereby reduce effects of motion of thenavigation unit on the interpretation of the received signals.
 8. Amethod of vehicle navigation using ATWD, comprising: storing informationof cultural or natural features in an area of the ground along and tothe sides of a movement path; using said ATWD during flight to obtaininformation of said features along and to the sides of said movementpath; comparing said stored information with said information obtainedby ATWD to determine match points representing different groundlocations; determining the range and range rate of said match points;and using said range and range rate in the navigation or state vectormeasurements of said vehicle.
 9. A method according to claim 8 wherein aphase changing effect created by motion of said navigation unit isreduced, said method comprising: defining subsets of a frequency hoppedATWD signal and a center frequency for each subset, bouncing said ATWDsignal off of an object and receiving a reflected version of said signalto determine a phase change in said signal; receiving reflected versionsof said subsets, and subtracting phase effects from the samples thatcorrespond to the center frequency of said subset.
 10. The method ofclaim 8 wherein phase compensation is provided for said ATWD, saidmethod comprising: storing a pseudo-random frequency order; changing atransmission frequency of a ATWD signal based on said pseudo-randomfrequency order; using said pseudo-random frequency order to sample areceived ATWD signal which is a reflected version of said transmittedATWD signal, thereby generating samples of said received ATWD signal;and reordering said samples in an order of increasing frequency.
 11. Themethod according to claim 10, further comprising: summing a group ofsaid samples; and performing an Inverse Digital Fourier Transform onsaid samples to develop a range value.
 12. The method according to claim10, further comprising: tagging said received samples with informationof their order of reception and order of frequency; identifying a coarserange of interest based on a particular area of the ground; selectingthe received samples which correspond to said particular area; definingsignal subsets of said transmitted signal; determining the Dopplerfrequencies for said selected samples and center frequencies for thesubsets corresponding to said particular area; arranging said receivedsamples in frequency order within said subsets; calculating the phaserotation due to transmit frequencies and receive times for the receivedsamples, and subtracting said phase rotations from the correspondingsamples according to the time order and wavelength of said sample, tothereby reduce effects of motion of the navigation unit on theinterpretation of the received ATWD signals.
 13. A method of using ATWDto improve the errors of a vehicle navigation system comprising: usingsaid ATWD during movement to obtain information of cultural or naturalfeatures in an area of the ground along and to the sides of the movementpath; dynamically creating a reference scene consisting of all or asubset of the said information; locally storing said reference scene;using said ATWD after a time delay to again obtain information of thesame said reference scene; comparing said stored information with saidinformation obtained by said ATWD to determine match points representingdifferent ground locations; determining the range and range rate of saidmatch points in both sets of information; and using said range and rangerate and the changes in range and range rate over said time in thenavigation or state vector measurements of said vehicle.
 14. The methodaccording to claim 13, further comprising: where the dynamically createdreference scene is sufficiently complex to be locally unique.
 15. Themethod according to claim 14, further comprising: where the dynamicallycreated reference scene is stored with match points and theircorresponding range and range rate data.
 16. A method according to claim1, wherein said ATWD is a sonar device.
 17. A method according to claim8, wherein said ATWD is a sonar device.
 18. A method according to claim13, wherein said ATWD is a sonar device.
 19. A method according to claim1, wherein said ATWD is a LASER device.
 20. A method according to claim8, wherein said ATWD is a LASER device.
 21. A method according to claim13, wherein said ATWD is a LASER device.
 22. A navigation systemcomprising: a memory that stores information of cultural or naturalfeatures in an area of the ground along and to the sides of a movementpath; an Active Traveling-Wave Device (ATWD) that obtains informationduring movement of said features along and to the sides of said movementpath; a processing system that compares said stored information withsaid information obtained by said ATWD to determine successive differentmatch points representing different ground locations, said processingsystem determining the range and range rate of each of said matchpoints, and determining the vehicle's location and velocity based onrepetitive range and range rate measurements of said match points. 23.The navigation system according to claim 22, wherein said processingsystem also inputs additional navigation unit information includingintegration of inertial measurements.
 24. The navigation systemaccording to claim 23, wherein said additional navigation unitinformation represents locations between said match points and iscombined with said vehicle location and velocity determination usingKalman statistical filtering.
 25. The navigation system according toclaim 22, wherein a phase changing effect created by motion of saidsystem is reduced, and wherein said processing system also definessubsets of a frequency hopped signal and a center frequency for eachsubset, bounces said signal off of an object and receives a reflectedversion of said signal to determine a phase change in said signal,receives reflected versions of said subsets, and subtracts phase effectsfrom the samples that correspond to the center frequency of said subset.26. The navigation system of claim 22 wherein phase compensation isprovided for said ATWD, and wherein said processing system stores apseudo-random frequency order in a memory, changes a transmissionfrequency of an ATWD signal based on said pseudo-random frequency order,uses said pseudo-random frequency order to sample a received ATWD signalwhich is a reflected version of said transmitted ATWD signal, therebygenerating samples of said received ATWD signal, and reorders saidsamples in an order of increasing frequency.
 27. The navigation systemaccording to claim 26, wherein said processing system sums a group ofsaid samples and performs an Inverse Digital Fourier Transform on saidsamples to develop a range value.
 28. The navigation system according toclaim 26, wherein said processing system tags said received samples withinformation of their order of reception and order of frequency,identifies a coarse range of interest based on a particular area of theground, selects the received samples which correspond to said particulararea, defines signal subsets of said transmitted signal, determines theDoppler frequencies for said selected samples and center frequencies forthe subsets corresponding to said particular area, arranges saidreceived samples in frequency order within said subsets, calculates thephase rotation due to transmit frequencies and receive times for thereceived samples, and subtracts said phase rotations from thecorresponding samples according to the time order and wavelength of saidsample, to thereby reduce effects of motion of the navigation unit onthe interpretation of the received signals.
 29. A vehicle navigationsystem, comprising: a memory that stores information of cultural ornatural features in an area of the ground along and to the sides of amovement path; an ATWD that obtains information during motion of saidfeatures along and to the sides of said movement path; a processingsystem that compares said stored information with said informationobtained by said ATWD to determine match points representing differentground locations, determines the range and range rate of said matchpoints, and uses said range and range rate in the navigation or statevector measurements of said vehicle.
 30. A navigation system accordingto claim 29 wherein a phase changing effect created by motion of saidnavigation unit is reduced, and wherein said system defines subsets of afrequency hopped ATWD signal and a center frequency for each subset,bounces said ATWD signal off of an object and receives a reflectedversion of said signal to determine a phase change in said signal,receives reflected versions of said subsets, and subtracts phase effectsfrom the samples that correspond to the center frequency of said subset.31. The navigation system of claim 29 wherein phase compensation isprovided for said ATWD, and wherein said navigation system stores apseudo-random frequency order, changes a transmission frequency of aATWD signal based on said pseudo-random frequency order, uses saidpseudo-random frequency order to sample a received ATWD signal which isa reflected version of said transmitted ATWD signal, thereby generatingsamples of said received ATWD signal, and reorders said samples in anorder of increasing frequency.
 32. The navigation system according toclaim 31, wherein said navigation system sums a group of said samples,and performs an Inverse Digital Fourier Transform on said samples todevelop a range value.
 33. The navigation system according to claim 31,wherein said navigation system tags said received samples withinformation of their order of reception and order of frequency,identifies a coarse range of interest based on a particular area of theground, selects the received samples which correspond to said particulararea, defines signal subsets of said transmitted signal, determines theDoppler frequencies for said selected samples and center frequencies forthe subsets corresponding to said particular area, arranges saidreceived samples in frequency order within said subsets, calculates thephase rotation due to transmit frequencies and receive times for thereceived samples, and subtracts said phase rotations from thecorresponding samples according to the time order and wavelength of saidsample, to thereby reduce effects of motion of the navigation system onthe interpretation of the received ATWD signals.
 34. A navigation systemfor a vehicle comprising: an ATWD operable to obtain information duringmovement of cultural or natural features in an area of the ground alongand to the sides of the movement path; a processing system thatdynamically creates a reference scene consisting of all or a subset ofthe said information, and locally stores said reference scene; saidnavigation system using said ATWD after a time delay to again obtaininformation of the same said reference scene, comparing said storedinformation with said information obtained by said ATWD to determinematch points representing different ground locations, determining therange and range rate of said match points in both sets of information,and using said range and range rate and the changes in range and rangerate over said time in the navigation or state vector measurements ofsaid vehicle.
 35. The navigation system according to claim 34, whereinthe dynamically created reference scene is sufficiently complex to belocally unique.
 36. The navigation system according to claim 35, whereinthe dynamically created reference scene is stored with match points andtheir corresponding range and range rate data.
 37. The navigation systemaccording to claim 22, wherein said ATWD is a sonar device.
 38. Anavigation system to claim 29, wherein said ATWD is a sonar device. 39.A navigation system to claim 34, wherein said ATWD is a sonar device.40. A navigation system to claim 22, wherein said ATWD is a LASERdevice.
 41. A navigation system to claim 29, wherein said ATWD is aLASER device.
 42. A navigation system to claim 34, wherein said ATWD isa LASER device.